Gender: Male 
Age: 35-44
Nationality: United States
Marital status: Single 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Undergraduate degree
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 4
Home: Rent 
Employment: Employed part time
Occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Devices used daily: Laptop;Smartphone;Smartwatch 
NLP tools known: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text) 
NLP tools used: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa) 
Frequency of LLM use: Nearly every day
LLMs used: Claude;ChatGPT 
LLM use cases: Writing (e.g., composing emails);Paraphrasing or finding synonyms;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Analyze data;Get information about current events;Learning (e.g., explaining complex concepts in simple terms);Answering questions about general knowledge;Collecting references;Completing assignments;Summarizing long texts;Solving math or logical problems 
Contexts of LLM use: Work;School/University;Creative or artistic;Personal;Learning 
Prompt 1: help create an invite for models to a creative shoot
Prompt 2: designing  a food festival
Prompt 3: problem gambler meaning
Prompt 4: i want a design for a creative project called Acapella Stories
Prompt 5: use this to create a new document with proposal for africhange citing them as our primary sponsors 
Prompt 6: how to calculate percentage
Prompt 7: 95 degree Fahrenheit in celsius 
Prompt 8: 0.375 as a fraction
Prompt 9: 6"3 in inches is
Prompt 10: 0.375 as a fraction